AI/ML Engineer - Python/NLP

Role Overview We are looking for an AI / ML Engineer who enjoys working in ambiguous, fast-moving environments and is excited to build, experiment, and problem-solve directly with founders. This is not a conventional tech team role. You will help identify opportunities where AI/ML can meaningfully improve product outcomes, internal efficiency, and learner experienceand then help build or prototype solutions.

Key Responsibilities

• AI / ML Initiatives :
• Identify and explore AI/ML use cases across learning, content, operations, and analytics.
• Build proof-of-concepts, prototypes, or lightweight production solutions.
• Work with structured and unstructured data (text, usage data, assessments, etc.).
• Apply ML techniques such as recommendation systems, NLP, predictive models, or evaluation frameworks (as relevant).
• Founders Office :
• Work directly with founders to translate business problems into AI-led solutions.
• Support experimentation and quick iterations rather than long, rigid roadmaps.
• Help evaluate third-party AI tools, APIs, and platforms for build vs buy decisions.
• Collaboration :
• Collaborate with product, content, and tech teams as needed (without direct reporting into Tech).
• Document learnings, trade-offs, and scalability considerations.

What Were Looking For

• Must-haves :
• Hands-on experience in AI / ML or Data Science.
• Strong fundamentals in Python, ML algorithms, and data handling.
• Experience working with ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch, etc.).
• Comfort working in low-structure, high-ownership environments.
• Ability to think beyond codeunderstanding why something should be built.
• Good to have :
• Experience with NLP, recommendation systems, or educational data.
• Exposure to LLMs, prompt engineering, or AI APIs.
• Startup or early-stage experience.
• Ability to communicate ideas clearly to non-technical stakeholders.

Why This Role Matters

• High visibility and direct founder interaction.
• Opportunity to shape AI thinking at an org-wide level.
• Space to experiment, learn fast, and build with real-world impact.
• Ideal for someone who wants to grow into AI Product / Strategy leadership over time. (ref:hirist.tech)